2.1. Python API

The following tutorial illustrates how to use the SBML2Julia Python API.

2.1.1. Importing SBML2Julia

Run this command to import SBML2Julia:

>>> import sbml2julia

2.1.2. Specifying an optimization problem

SBML2Julia uses the PEtab format for specifying biological parameter estimation problems. PEtab is built around SBML and based on tab-separated values (TSV) files. Please visit the PEtab documentation and have a look at the PEtab examples for detailed instructions on how to specify an optimization problem in PEtab. If you also want to customise upper and lower boundaries for the model species, you can provide an additional species table (see species_Vinod_FEBS2015.tsv as an example).

SBML2Julia also contains the following optimization hyperparameters:

• t_steps: number of time-discretization steps. Default None.

• n_starts: number of multistarts. Default 1.

• infer_ic_from_sbml: infer initial conditions which are not specified in the PEtab condition table from SBML. Default False.

• optimizer_options: optimization solver options. Default {}.

• custom_code_dict: dict with replaced code as keys and replacement code as values. Default {}.

The problem is then specified as:

>>> problem = sbml2julia.SBML2JuliaProblem('my_petab_promlem.yaml', t_steps=100, n_starts=1, infer_ic_from_sbml=False, optimizer_options={}, custom_code_dict={})

Once the problem is specified, sbml2julia has transformed the problem to a julia JuMP model. The code for this model can be accessed via:

>>> code = problem.julia_code

or written to a file via:

>>> problem.write_jl_file(path='path_to_jl_file.jl')

If you want to change the optimization problem in a way that is not yet supported by SBML2Julia, you can manually modify the julia code and run the optimization in julia yourself. Alternatively, you can change problem.julia_code via:

>>> problem.insert_custom_code({'<replaced lines>': '<replacement lines>'})

2.1.3. Choosing an HSL linear solver

Optionally, the optimizer_options attribute can be used to specify the linear solver used within Ipopt. For example:

>>> problem.optimizer_options={'linear_solver': 'ma57'}

2.1.4. Running the optimization

The optimization can be run with:

>>> problem.optimize()

Please note that this may take a while.

2.1.5. Accessing the results

The results can be accessed via:

>>> results = problem.results

and written to TSV and Excel files with:

>>> problem.write_results(path='./tsv_results/')
>>> problem.write_results(path='results.xlsx')

Time courses for the optimal solution of condition cond and corresponding experimental datapoints can be plotted by:

>>> problem.plot_results(cond, path='path_to_plot.pdf', observables=[], size=(6, 5))

where the optional observable argument accepts a list of observables that shall be plotted (if emppty, all observables specified in PEtab are plotted). The optional size argument specifies the size of the figure.

If you want to update the nominal parameter values in your PEtab problem parameter table with the fitted values, run:

>>> problem.write_optimized_Parameter_table()

This will create a post_fit_parameters.tsv file in your PEtab problem directory. This can be useful to perform sensitivity analysis in other PEtab compatible optimization toolboxes such as pyPesto.